Human Centered AI Lab
The HumanAI Research Lab of the Department of Computer Science and Engineering, University of Moratuwa, advances research in human-centered artificial intelligence through interdisciplinary research, technological innovation, and collaborative exploration. The lab focuses on developing trustworthy AI systems that enhance human capabilities, address complex real-world challenges, and create meaningful impact across diverse domains.
The lab envisions a future in which artificial intelligence technologies are designed with humans at the center, supporting inclusive, ethical, and sustainable progress. Its broader goal is to contribute to excellence in AI research, education, and innovation while addressing both global advancements and local and societal needs.
Research at the HumanAI Research Lab operates across the boundaries of multiple disciplines, with particular emphasis on the intersections between technology and human behaviour. Rather than treating these areas independently, the lab investigates how they can be combined to create intelligent systems that are effectively integrated into human society.
Research Themes
Model Building and Model Engineering
Model Building and Model Engineering encompasses the systematic process of constructing, validating, and optimising mathematical, statistical, and machine learning models for real-world problem solving. Researchers in this area work across the full modelling lifecycle — from problem formulation and data-driven model design to parameter tuning, uncertainty quantification, and model evaluation. Emphasis is placed on developing models that are not only accurate but also computationally efficient, generalizable, and interpretable. Key methodologies include differential equation modelling, probabilistic graphical models, neural architecture design, ensemble methods, and simulation-based modelling. This area serves as the theoretical and methodological backbone for applied research across all other focus areas within the department.
Applied AI with Domains
Applied AI with Domains focuses on the cross-disciplinary integration of artificial intelligence and machine learning methodologies into specialized fields to solve domain-specific problems. Researchers collaborate with industry partners and domain experts to identify high-impact challenges and develop AI-driven solutions tailored to the constraints and requirements of each application area. Research activities span predictive analytics, computer vision, natural language processing, optimization, and decision support systems deployed within sectors such as transportation, healthcare diagnostics, agricultural monitoring, financial forecasting, and smart manufacturing. A core objective of this area is to ensure that AI solutions are not only technically sound but also practically viable, ethically responsible, and contextually appropriate for real-world deployment.
AI Governance and Policy Making
AI Governance and Policy Making addresses the growing need for structured oversight of AI systems as they become increasingly embedded in critical societal functions. Research in this area investigates the ethical implications of AI decision-making, the development of fairness and bias mitigation frameworks, transparency and explainability standards, and the design of regulatory instruments suitable for rapidly evolving AI technologies. Interdisciplinary in nature, this area draws from computer science, law, philosophy, economics, and public policy to produce actionable governance models and policy recommendations. Researchers engage with government bodies, standards organisations, and civil society to translate technical insights into policy-relevant outputs, contributing to the responsible and inclusive development of AI at both national and international levels.
Multi-Agent Systems and Applications
Multi-Agent Systems (MAS) and Applications focuses on the design, analysis, and deployment of systems in which multiple autonomous agents — each with their own goals, knowledge, and decision-making capabilities — interact within a shared environment to achieve individual or collective objectives. Research spans foundational topics including agent architectures, communication protocols, negotiation mechanisms, game-theoretic modelling, and emergent behaviour, as well as applied domains such as autonomous vehicle coordination, smart grid management, supply chain optimization, robotic swarms, and distributed simulation environments. A key research challenge addressed in this area is enabling agents to operate effectively under uncertainty, with incomplete information, and in dynamic environments. MAS research at the department aims to develop scalable, robust, and verifiable multi-agent frameworks with direct applicability to complex real-world systems.
Research Leadership
Director :Prof. Indika Perera
Staff Members
Prof. Dulani Meedeniya
Dr. Kutila Gunasekara
Dr. Adeesha Wijayasiri
Dr. Chathuranga Hettiarachchi
Ms. Gayani Wickramarathna
Mr. Savindu Rajapaksha
More Information
For further information on research projects, publications, researchers, and activities, visit the HumanAI Research Lab website: